Consistent Online Multi-object Tracking with Part-Based Deep Network

Abstract

Multi-object tracking is still a challenge problem in complex and crowded scenarios. Mismatches will always happen when objects have similar appearance or are occluded with each other. In this paper, we appeal for more attention to the consistency of the trajectories and propose a part-based deep network which employs ROI pooling method to extract full and part-based features for the objects. An occlusion detector is proposed to predict the occlusion degree and guide the procedure of part-based feature fusion and appearance model update. In this way, the feature extraction speed of our tracker is faster, and the objects can be associated correctly even if they are partly occluded. Besides, we train the network based on siamese architecture to learn a dissimilarity metric between pairs of identities. Extensive experiments with multiple evaluation metrics show that our tracker can associate the objects consistently and gain a significant improvement in tracking accuracy.